As illustrated in FIG. 1, it is frequently carried out that data representing a circuit to be simulated and device characteristic parameters are inputted to a simulator such as a Simulation Program with Integrated Circuit Emphasis (SPICE) to cause the simulator to simulate operations of the circuit, and from the simulator, the judgment results of the operations and/or circuit characteristic values for the inputted circuit and device characteristic parameters are obtained. Incidentally, for example, the judgment result of the operations is grasped as the yield. In addition, the circuit characteristic values obtained by the simulation may be called “performance”.
Recently, because the microfabrication of the semiconductor advances, the influence of the variability of the device characteristic on the yield is increasing. Therefore, as schematically depicted in FIG. 2, a “variability-aware simulation” is carried out that data (also called “statistical parameters”) concerning distribution of the device characteristic is inputted to the simulator to calculate the predicted yield value. In such a “variability-aware simulation”, it is important how to match up the distribution of the device characteristic to the actual distribution, and the setting of the distribution of the device characteristic largely influences the accuracy of the yield calculation. However, there is a problem that the deviation between the predicted yield value outputted from the simulator and the yield measurement value is large.
Then, there is a case where the statistics of the measurement values are used as the distribution of the device characteristic to be inputted to the simulator. However, even if such data is used, the deviation between the predicted yield value and the yield measurement value is not cancelled due to some reasons, for example, there are conditions and/or characteristics, which are not measured, and/or the number of measurement samples is very small. Especially, as for the number of measurement samples, in case of a Static Random Access Memory (SRAM) having a capacity of 10M bits, in order to predict whether or not the yield is equal to or greater than 99%, the device characteristic has to be measured 10M*100=109 times. This is not a realistic value.
Therefore, as schematically depicted in FIG. 3, there is a technique to search statistical parameter values of the device characteristics, which are input values to the variability-aware simulator, so as to match up the predicted yield value, which is an output of the variability-aware simulator, to the “yield measurement value”. However, because the yield is determined as a result based on the huge number of circuit characteristic values, a lot of combinations of the statistical parameter values, which give the same yield, generally. Namely, because the search space is broad, it is realistically impossible to select an appropriate combination of the statistical parameter values. Therefore, as schematically depicted in FIG. 3, there are a lot of cases that a physically or empirically unsuitable value is obtained for the statistics of the measurement values of the device characteristics. Moreover, because the measurement errors influence, it is difficult for such a technique to obtain an appropriate statistical parameter values of the device characteristics.